Measures of cognitive performance are subject to age-related changes. In addition to changes in the level of these measures, there are also consistent increases in intraindividual variability. Understanding the meaning of these age-related changes in intraindividual variability is important both for a basic understanding of cognitive aging and for understanding of the diagnostic utility of variability measures. Theoretical accounts for age-related increases in intraindividual variability often refer to time dependent processes such as fluctuations or lapses of attention. Data in this context are rarely analyzed as time series despite this being the most appropriate form to reveal such processes. We propose the application of time series analyses to explore the sequential relations in performance for young and older adults. We show preliminary evidence that time series data from both young and older adults are long range dependent (LRD). LRD refers to the fact that relations can be observed in time series data spanning many trials. Temporal aggregation of component cognitive processes can produce LRD and the strength of LRD is influenced by the operation of longer time scale processes that include executive control processes that act to maintain performance over periods of time. Thus, LRD may be useful for understanding how these processes are influenced by variations in processing demands and the aging process. However, tools that have been used to model these dependencies called ARFIMA models make assumptions of stationarity that do not hold for behavioral data. We propose SEMIFAR models as better suited to measuring LRD and are robust in the face of nonstationarity of behavioral data. We also propose the application of GARCH/ARCH models that capture sequential relations in variability. Preliminary data suggests the time series for young and older adults show volatility clustering. In other words, high and low variability periods cluster together within an individual's time series. Together, these models provide robust methods for exploring the contribution of time dependent processes to intraindividual variation. Four experiments will be analyzed using these models to account for age differences in these components of intraindividual variabilty. These experiments explore manipulations designed to influence sequential dependencies within young and older adults and extend our understanding of sources of intraindividual variability. [unreadable] [unreadable] [unreadable]